Tourism ranks among the world's largest industries, and its sustained expansion has paralleled swift advancements in technology. Artificial Intelligence (AI) is increasingly recognized as a transformative force in tourism, offering human-like capabilities that enhance decision-making and service automation. Its application across the sector improves operational efficiency and personalizes customer experiences, thereby fostering innovation and competitiveness. However, the rapid integration of AI also presents conceptual, theoretical, and societal challenges that require critical examination. The research aims to synthesize the conceptual and theoretical research on AI in tourism from 2019 onwards. It examines key themes, theoretical perspectives, methodological rigor, and research gaps in the existing literature. Further goal is to identify thematic areas with a specific focus on AI applications. The study followed the PRISMA guidelines to conduct a systematic literature review (SLR). Academic databases, including Scopus and Web of Science, were searched to identify scientific-relevant peer-reviewed articles. From an initial pool of over 400 studies, we identified 45 significant journal articles and selected them for an in-depth analysis, that collectively illuminate how AI is reshaping tourism research and practice. Studies have drawn on innovation diffusion theory to explain adoption patterns, technology acceptance models to gauge user and employee attitudes, and service quality and co-creation theories to understand how AI can add value to the customer experience. It also highlighted the evolution of AI research in tourism, from conceptual discussions to empirical investigations. Gaps and challenges in the research were identified, including a limited focus on human-AI interaction, ethical concerns, and methodological rigor. The review concludes that AI has the potential to transform tourism by enhancing efficiency, personalization, and sustainability. The findings reveal that AI has been envisioned as a catalyst for transformation in the tourism industry, with applications ranging from intelligent forecasting and revenue management to service automation via robots and hyper-personalized travel experiences. AI-driven analytics can improve decision support for revenue management, capacity planning, and marketing strategy. However, realizing this potential requires addressing the improvement of technological competence of human resources, ethical issues, and implementation strategies.

Industry 4.0 focuses on the digitalization of production processes and technological innovation. The concept of Industry 5.0 puts the focus on human-centricity, sustainability, and resilience at the heart of research and development and innovation (R&D&I) processes to allow industry to serve humanity with a long-term vision that considers planetary boundaries. Replacing the technology-driven approach with a fundamentally human-centric approach requires a deep understanding of the working environment and workers interacting with machines to optimize worker well-being, working conditions, and job outcomes. Analyzing computer work User eXperience (UX) in industrial environments is vital. However, user perceptions are usually hidden and a challenge to detect. Therefore, measuring and monitoring perceptions, emotional reactions, subjective elements, preferences, and attitudes in the relationships between usability, work performance, and workload is crucial. This study provides conclusions of a literature review on user experience studies focusing on UX testing methods and the disciplines linked to industrial diversification. Based on literature analysis, it identifies UX testing methods and create own grouping to analyze them. It also examines the disciplinary context of user experience testing.